Uncompressed digital video data used by a regular digital video device has a very large size, and normally includes multiple scenes, where each scene is composed of a set of similar frames, and therefore, detection of a scene change can be beneficial to digital video processing.
Scene change detection is necessary for functions related to extracting key frame, highlighting, and indexing in a digital video sequence. These functions can be widely utilized in personal devices such as camcorders, video recorders and video players, and in specialized systems for digital video browsing, recognition, tracking, monitoring and intrusion blocking, and the like.
Currently, scene change detection is used in diverse multimedia services based on digital videos in relation to highlight editing, scene segmentation, categorization, recognition, browsing, monitoring, blocking and tracking, etc.
There are many existing approaches to scene change detection. For example, in one approach for video file identification, an input video sequence is subdivided into still images, a key image is semi-automatically selected in accordance with user needs or administrator needs, a set of video files are selected on the basis of the key image, and the images of the input video sequence are compared with those of the selected video files to find a match.
In another approach, the differential value of mean absolute difference between image frames is calculated, and thresholding is applied to the calculated differential value to detect a scene change. Scene change information of the previous frame is utilized to detect a scene change in the current frame.
In yet another approach, one-dimensional histograms of Y, Cb and Cr values of pixels are computed for each video frame, and a ratio of distances between the histograms of consecutive frames is used to detect a scene change.
In still another approach for an MPEG compression-coded video stream, histograms are derived nonlinearly from DC coefficients of I frames in accordance with visual properties, and distances therebetween are used to detect a scene change.
In a further approach, differences between color histograms of three consecutive frames are computed, and arrangement properties of the differences are used to detect a scene change. In addition, the distribution of macroblock types is analyzed to verify the presence of a scene change.
In a still further approach, luminance and chrominance histograms of consecutive frames are derived, and the cross-correlation therebetween is used to detect a scene change.
In another approach for an MPEG compression-coded video stream, edge information is derived using DCT AC coefficients, and variations of the edge information are used to detect a scene change. To reduce false detections due to object motion, after motion compensation is performed, a comparison is made between histograms of edge images to measure variations.
In yet another approach, errors resulting from estimation of motion vectors in video compression are used to detect a scene change.
In still another approach for indexing key frames using adaptive scene change detection, videos are categorized into genres including football, news, music, document, animation, and show. Then, for a video stream, local variance, histogram, entropy coding and camera motion are selectively utilized in accordance with each of the genres to detect a scene change.
In a further approach, the ratio of the sum of absolute pixel differences to the sum of absolute histogram differences, between consecutive frames, is used to detect a scene change.
A still further approach computes the rate that pixel variations between consecutive frames are larger than a threshold value, and uses a scene change detection filter to check an occurrence of a scene change.
Another approach for an MPEG compression-coded video stream computes mean-square differences between pixel values of DCT coefficients for I frames, counts the number of forward-predicted macroblocks for P frames, and counts the number of forward-predicted macroblocks and the number of backward-predicted macroblocks for B frames, to detect a scene change.
In another approach, various computable video features are proposed to classify films into genres. To detect a scene change, HSV color histograms of 16 bins (8 for the hue, 4 for the saturation, and 4 for the value components of the HSV color space) are calculated for each frame, and the intersection of the histograms of consecutive frames is computed and anisotropic diffusion algorithm is applied to detect seen changes. The detected scene changes are used to classify films into genres.
As described above, there are many existing schemes for scene change detection. However, in the existing schemes, spatial and temporal redundancy in a video stream is not utilized, and various local errors that may happen in a certain video stream are not handled adequately.
Existing systems for scene change detection may be unable to set a scene detection state in accordance with the system characteristics and properties of a digital video. Hence, considering the time taken for analysis and determination related to scene detection, they may produce poor scene detection results.